Amplitude-controllable event-driven organic photosensors based on ionic-mediated inhibition
Abstract Next-generation artificial vision systems must integrate rapid temporal-contrast detection with dynamic sensory-gain modulation to prioritize task-relevant stimuli. Conventional semiconductor photosensors lack intrinsic mechanisms for spike generation with tunable amplitude, requiring complex multitransistor architectures and limiting efficiency. Here we report an event-driven organic photosensor that integrates temporal-contrast detection and amplitude modulation within a single active layer by coupling ionic and electronic transport in an organic mixed ionic–electronic conductor. In a bulk heterojunction with a non-fullerene acceptor, fast electron extraction generates excitatory photocurrent spikes, whereas ion-compensated hole accumulation in the organic mixed ionic–electronic conductor donor provides voltage-tunable inhibitory control over spike amplitude—functionally analogous to attentional gain modulation in biological vision. This enables in-sensor amplitude–temporal coding, preserving motion-relevant contrast in bias-weighted optical events and reducing redundant read-out. These results establish ionic–electronic coupling in organic mixed ionic–electronic conductors as a materials strategy for adaptive, low-power neuromorphic vision hardware.
Authors
- Qunping Fan (ORCID: https://orcid.org/0000-0002-3762-1340)
- Bingjun Wang (ORCID: https://orcid.org/0000-0001-5832-4081)
- Wei Ma (ORCID: https://orcid.org/0000-0002-7239-2010)
- Tianming Li (ORCID: https://orcid.org/0000-0002-8694-6643)
- Zhongrui Wang (ORCID: https://orcid.org/0000-0003-2264-0677)
- Sen Zhang (ORCID: https://orcid.org/0000-0002-1716-3741)
- Chao Zhao (ORCID: https://orcid.org/0000-0002-9916-9804)
- LI Xu-hui
- Shijie Wang
- Xudong Su
- Xi Chen
- Jinjian Shen
- Ning Lin
- Yuxing Tao
- Zixuan Yuan
Institutions
- Northwestern Polytechnical University (CN)
- Southern University of Science and Technology (CN)
- Xi'an Jiaotong University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Nature Materials
- Published
- 2026-09-30
- DOI
- https://doi.org/10.1038/s41563-026-02747-8
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00